Conceived and designed the experiments: EA JiK YA TO YM KY. Performed the experiments: JiK YA TO. Analyzed the data: KF JiK YA. Contributed reagents/materials/analysis tools: JiK YA TO. Wrote the paper: KF KY.
Shimadzu corporation possesses patents about the FF-2A odour discrimination device. Representative Patents: Analysis algorithms (2005-291763, 2005-077099, 2004-93447, 2004-93446, and 2003-315298); Odour capture system (2008-145271); Odour discrimination device systems (2004-333134). These algorithms and systems have limitation of use.
Smell provides important information about the quality of food and drink. Most well-known for their expertise in wine tasting, sommeliers sniff out the aroma of wine and describe them using beautiful metaphors. In contrast, electronic noses, devices that mimic our olfactory recognition system, also detect smells using their sensors but describe them using electronic signals. These devices have been used to judge the freshness of food or detect the presence of pathogenic microorganisms. However, unlike information from gas chromatography, it is difficult to compare odour information collected by these devices because they are made for smelling specific smells and their data are relative intensities.
Here, we demonstrate the use of an absolute-value description method using known smell metaphors, and early detection of yeast using the method.
This technique may help distinguishing microbial-contamination of food products earlier, or improvement of the food-product qualities.
Since the deterioration and quality variation of food and drink are often associated with microbial activity, several previous reports have attempted to analyze the smells produced by different microorganisms. They used gas chromatography (GC)/mass spectrometry (MS) techniques to detect and discriminate between these scents
Research has tested the validity of using electronic noses in a variety of applications
However, two important obstacles still prevent the routine use of electronic nose measurements: 1. we cannot identify what differences there are between smells, and 2. we cannot compare and assemble the data collected between different electronic noses. The reason behind these obstacles stems from the fact that the data generated by different electronic noses are sensor specific. Moreover, current electronic noses are designed to detect certain smells using different sensors. To understand the difference and to discriminate between various smells, should we prepare many sensors against possible smells?
To solve this problem even in cases using a small number of sensors, we propose using a new smell description method that combines smell intensity and smell specifications analysis, akin to how sommeliers describe the aroma of wine.
First, in order to express the criteria of smell intensity as an absolute value, we propose the development of a standard odour index. The odour index concept in this study was originally introduced in the Japanese Offensive Odour Control Law (1971) and has been used as a means to measure environmental odours. It includes information on how much a smell sample can be diluted to reach the human nose threshold, the lowest concentration which human noses can detect. The odour index is defined as 10·log10 (dilution rate). This formula was derived by the careful analysis of the human olfactory recognition system
Our second consideration was the criteria of smell specification. We proposed describing this aspect of odour using known smell categories provided by standard gasses (metaphor expression). Using the known smell information provided by standard gases, the amount of accessible information we can use for describing smells can increase dramatically. There are, for example, two common ways to describe the flavour of tea. ‘This flavour is sweet’ and ‘This flavour is like muscat’; the latter metaphor description enables us to imagine the flavour better.
We enabled an electronic nose, FF-2A (Shimadzu Corporation, Japan)
The FF-2A electronic nose recognized odours and calculate the odour indices as described below. The device contained 10 metal oxide semiconductors sensors with different sensitivities and selectivity for different fragrant substances (
(a) Nine standard gases were introduced to the smell sensor array in FF-2A electronic nose system. The FF-2A used multivariate analysis to calculate the vectors of the standard gases. After yeast volatile samples were introduced and their vectors were calculated, the standard gas vectors were used to calculate the indices and similarities of yeast volatile samples. (b) Calculation method of odour index, total odour index and similarity. After all the vectors were calculated (left), the indices were calculated using virtual sample concentrations for each axis (right). Similarity was calculated using the angles between the different sample vectors.
Moreover, the FF-2A was installed with a trap tube for concentrating smells and removing water vapour, which can affect the measurement value (
In this study, we challenged FF-2A with early detection of yeast and confirmed whether the combination method, absolute-value intensity and metaphor specification, is useful for detection and discrimination of microorganisms. To determine the lowest yeast concentration detectable, we measured the volatiles from samples obtained from 102 to 107 cfu/ml in the Glucose-Yeast-Peptone (GYP) media. In addition, to investigate the advantage of combining intensity and specification, we tested 2 other methods, i.e. total odour index (using smell intensity only) and similarity against other samples (using smell specification only).
Saccharomyces sp. (yeast) and Lactococcus lactis SNW-1 (lactic acid bacteria) were provided by Sanwa Norin Co. Ltd. These microorganisms were cultured in GYP media (1.0% glucose, 0.5% yeast extract, 0.5% peptone, 0.01% MgSO4, 0.0005% MnSO4, 0.0005% FeSO4, and 0.002% NaCl). Both microorganisms were cultured to 1.0 McFarland and then diluted to the indicated concentrations with the media.
The electronic nose was calibrated using the 9 standard gases as described, following which 2 ml of the samples (consisting of microorganisms and medium) were collected in 2-liter PET bags filled with dry nitrogen. The bags were allowed to equilibrate for 1 h at 25°C. The headspace volatiles were collected and diluted with dry nitrogen in new 2-liter PET bags. These diluted samples were introduced into the trap tube for 60 s and then exposed to the array with pure nitrogen gas. All the samples were measured four times and the final three measurements were used for analysis. Approximately 90 min were required to obtain the first data reading.
The virtual concentration for each standard-gas axis was calculated by projecting the vector obtained to the axis (
Cs represented the virtual concentration compared to the standard-gas axis and Ct was used to describe the threshold concentration (the lowest detectable concentration) of the standard gas by the human nose.
The total odour index, or the smell intensity as a whole, was calculated from the summation of the 9 standard gas intensities as in equation (2).
The similarity was calculated using the angles between the sample vectors. For this calculation we used the following criteria; θ = 0°, similarity 100%; θ>20°, similarity 0%.
Using the combination method, we showed the odour indices of the yeast volatiles in terms of the 9 standard gases categories (
The graph (a) and radar chart (b) depict the values calculated by the odour index expressed as mean±standard deviation (n = 3). GYP indicates the GYP control medium. Lact 107 indicates the volatile sample from 107 cfu/ml of lactic acid bacterium in the GYP medium (bacterium control).
In
We described the data from yeast volatiles using the total odour index as the method we did with smell intensity only (
These data were calculated using the smell intensities of all the volatiles in combination. The odour data are expressed as mean±standard deviation (n = 3). The approximation curve and correlation coefficient (R2) were calculated using Microsoft Excel 2003.
The yeast volatiles were compared to the GYP control media (a) and yeast volatiles generated by 107 cfu/ml (b). The similarities were calculated using only smell specification. The data are expressed as mean±standard deviation (n = 3). Lact indicates the volatile samples collected from 102 to 107 cfu/ml lactic acid bacteria (bacterium control).
The similarities of the yeast volatiles obtained from 102 to 107 cfu/ml compared to the GYP media alone showed that they were all less than 84% and therefore, clearly different from the GYP media (99.3%;
In this paper, we challenged early detection of yeast and compared the three methods to detect and discriminate yeast cultures using an electronic nose. The combination method that uses absolute-value intensity and metaphor specification, was extremely sensitive and could detect and discriminate yeast at the same time. Moreover, approximately 90 min were required from the sample collection to the first data reading. This shorter time analysis may help fresh food administration especially. Meanwhile, using the total odour index and similarity alone proved to be highly concentration dependent and could detect some differences. However, we could not discriminate between yeast and lactic acid bacteria using solo methods. We should combine the methods, smell intensity and specification, to detect and discriminate microorganisms at the same time.
Absolute-value smell will help record and compare smells in the development of food and drink products. For example, in the flavour of cheeses and other fermented foods, in addition to consistency and quality, there is a growing consumer demand for a larger diversity
Furthermore, the absolute-value smell will compare the data from an electronic nose with the data from other electronic noses. The data from current electronic noses are relative values, since they have different sensors in each nose. The concept of absolute value smell is applicable to other electronic noses and will help gather data and build data bases.
For more useful information, selection of appropriate standard gasses remains as one of the key issues to be clarified. In this study, we selected the gasses from odorants related to environment offensive odours. In order to detect odorant from microorganisms sharply, GC or GC/MS data in past and current odour studies will support the selection.
Although we examined only yeast and lactic acid bacteria in this report, if scientists continue to collect smell data from different microorganisms using these methods, the resulting database will undoubtedly prove helpful in improving the safety and quality control of foods and drink. To compare and assemble such smell data, we believe that the key is using absolute values and propose the use of our combination method for the measurement of smells to assemble these databases.
We thank Dr. Tadayoshi Tanaka (Kyoritsu Women's University) for his useful advice regarding volatiles produced by bacteria. Ms. Miki Noji (Tokyo University of Agriculture) for providing useful bacterial knowledge and Mr. Kazuyuki Ito (IMCJ) for helping us to revise our manuscript.